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434 Publications


2018 | Journal Article | LibreCat-ID: 16038
Schäfer, D., & Hüllermeier, E. (2018). Dyad ranking using Plackett-Luce models based on joint feature representations. Machine Learning, 107(5), 903–941.
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2018 | Conference Paper | LibreCat-ID: 2109
Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). Ensembles of Evolved Nested Dichotomies for Classification. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM. https://doi.org/10.1145/3205455.3205562
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2018 | Conference Paper | LibreCat-ID: 2471
Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). On-The-Fly Service Construction with Prototypes. In SCC. San Francisco, CA, USA: IEEE Computer Society. https://doi.org/10.1109/SCC.2018.00036
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2018 | Book Chapter | LibreCat-ID: 6423
Schäfer, D., & Hüllermeier, E. (2018). Preference-Based Reinforcement Learning Using Dyad Ranking. In Discovery Science (pp. 161–175). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-01771-2_11
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2018 | Preprint | LibreCat-ID: 17714
Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). Automated machine learning service composition.
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2018 | Journal Article | LibreCat-ID: 10276
Schäfer, D., & Hüllermeier, E. (2018). Dyad Ranking Using Plackett-Luce Models based on joint feature representations. Machine Learning, 107(5), 903–941.
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2018 | Conference Paper | LibreCat-ID: 10149
Hesse, M., Timmermann, J., Hüllermeier, E., & Trächtler, A. (2018). A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. In Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected Systems in Products and Production, Procedia Manufacturing 24 (pp. 15–20).
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2018 | Book Chapter | LibreCat-ID: 10783
Couso, I., & Hüllermeier, E. (2018). Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators. In S. Mostaghim, A. Nürnberger, & C. Borgelt (Eds.), Frontiers in Computational Intelligence (pp. 31–46). Springer.
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2018 | Conference Paper | LibreCat-ID: 2857
Mohr, F., Lettmann, T., Hüllermeier, E., & Wever, M. D. (2018). Programmatic Task Network Planning. In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (pp. 31–39). Delft, Netherlands: AAAI.
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2018 | Conference Paper | LibreCat-ID: 3552
Mohr, F., Wever, M. D., & Hüllermeier, E. (n.d.). Reduction Stumps for Multi-Class Classification. In Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands. https://doi.org/10.1007/978-3-030-01768-2_19
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2018 | Conference Paper | LibreCat-ID: 10145
Ahmadi Fahandar, M., & Hüllermeier, E. (2018). Learning to Rank Based on Analogical Reasoning. In Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI) (pp. 2951–2958).
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2018 | Book Chapter | LibreCat-ID: 10152
Mencia, E. L., Fürnkranz, J., Hüllermeier, E., & Rapp, M. (2018). Learning interpretable rules for multi-label classification. In H. Jair Escalante, S. Escalera, I. Guyon, X. Baro, Y. Güclüütürk, U. Güclü, & M. A. J. van Gerven (Eds.), Explainable and Interpretable Models in Computer Vision and Machine Learning (pp. 81–113). Springer.
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2018 | Conference Abstract | LibreCat-ID: 1379
Seemann, N., Geierhos, M., Merten, M.-L., Tophinke, D., Wever, M. D., & Hüllermeier, E. (2018). Supporting the Cognitive Process in Annotation Tasks. In K. Eckart & D. Schlechtweg (Eds.), Postersession Computerlinguistik der 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft. Stuttgart, Germany.
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2018 | Journal Article | LibreCat-ID: 3510
Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). ML-Plan: Automated Machine Learning via Hierarchical Planning. Machine Learning, 1495–1515. https://doi.org/10.1007/s10994-018-5735-z
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2018 | Bachelorsthesis | LibreCat-ID: 5693
Graf, H. (2018). Ranking of Classification Algorithms in AutoML. Universität Paderborn.
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2018 | Journal Article | LibreCat-ID: 3402
Melnikov, V., & Hüllermeier, E. (2018). On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning. https://doi.org/10.1007/s10994-018-5733-1
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2017 | Conference Paper | LibreCat-ID: 10204
Ewerth, R., Springstein, M., Müller, E., Balz, A., Gehlhaar, J., Naziyok, T., … Hüllermeier, E. (2017). Estimating relative depth in single images via rankboost. In Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017) (pp. 919–924).
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2017 | Conference Paper | LibreCat-ID: 10209
Ahmadi Fahandar, M., & Hüllermeier, E. (2017). Learning to Rank based on Analogical Reasoning. In Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence.
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2017 | Conference Paper | LibreCat-ID: 10216
Shaker, A., Heldt, W., & Hüllermeier, E. (2017). Learning TSK Fuzzy Rules from Data Streams. In Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Skopje, Macedonia.
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2017 | Conference Paper | LibreCat-ID: 1180
Wever, M. D., Mohr, F., & Hüllermeier, E. (2017). Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. In 27th Workshop Computational Intelligence. Dortmund.
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